{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fall / Non Fall Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Training and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Rows, Columns) in training dataset (480, 136)\n",
      "Number of Features in training dataset:- 136\n",
      "Number of Features in testing dataset:- 136\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,AdaBoostClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn import ensemble\n",
    "import xgboost as xgb\n",
    "from sklearn.feature_selection import VarianceThreshold, RFE\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Random values generated should be same everytime the program is run\n",
    "np.random.seed(7)\n",
    "\n",
    "#Reading of Training and Testing Data from CSV File\n",
    "data=pd.read_csv(\"Dataset.csv\")\n",
    "train=data.iloc[0:480,:]\n",
    "test=data.iloc[480:,:]\n",
    "print(\"(Rows, Columns) in training dataset\",train.shape)\n",
    "print(\"Number of Features in training dataset:-\",train.shape[1])\n",
    "print(\"Number of Features in testing dataset:-\",test.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "Replace NaN values with minimun of that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "#Transforming Columns containg \"NaN\" Values\n",
    "#in both training and testing dataset\n",
    "for col in train.columns:\n",
    "    val = train[col].min()\n",
    "    train[col]=train[col].fillna(value=val,axis=0)\n",
    "\n",
    "for col in test.columns:\n",
    "    val = test[col].min()\n",
    "    test[col]=test[col].fillna(value=val,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Train and Test Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Separating training data Features and Predictions into different dataframes\n",
    "X_train=train.drop('class_label',axis=1)\n",
    "Y_train=train['class_label']\n",
    "\n",
    "#Separating testing data Features and Predictions into different dataframes\n",
    "X_test=test.drop('class_label',axis=1)\n",
    "Y_test=test['class_label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc12c74fa90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sQZ9kDfB3wKXAucBVSc4dx3tJkhY2riP684Dpqnq8qv4XuA3YMqb3kiQtYMk3Bx9iHbBv\n3voM8K75A5JsA7a11eeSPDqmXk5EZwI/X+0mhstqN6Bj7/j42cxx87P5u6MMGlfQD/qvVL+xUrUd\n2D6m9z+hJZmqqsnV7kM6kj+bq2NcUzczwIZ56+uB/WN6L0nSAsYV9N8FNiU5J8kpwJXAzjG9lyRp\nAWOZuqmqF5JcB/wrsAa4tar2jOO9NJBTYnql8mdzFaSqho+SJB23vDJWkjpn0EtS5wx6SercuM6j\n1zGU5C3MXXm8jrnrFfYDO6tq76o2JukVwSP641ySjzH3FRMBHmLu1NYAX/XL5CSBZ90c95L8J/C2\nqvq/I+qnAHuqatPqdCYdXZKrq+qLq93HicIj+uPfi8AbB9TXtm3SK9GnVruBE4lz9Me/DwP3JHmM\nl75I7neANwPXrVpXOuEl2X20TcDZx7KXE51TNx1I8irmvhp6HXP/E80A362qQ6vamE5oSZ4E3gM8\nfeQm4D+qatC/RDUGHtF3oKpeBB5Y7T6kI9wJvLaqdh25Icn9x76dE5dH9JLUOf8YK0mdM+glqXMG\nvSR1zqCXpM79P/hY7DvHtOZyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc12c74f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.class_label.value_counts().plot(kind='bar',colors='YR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection\n",
    "### Remove Low Variance Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Features in training dataset(before feature selection by Variance Threshold):- 135\n",
      "Number of Features in training dataset(after feature selection by Variance Threshold):- 127\n"
     ]
    }
   ],
   "source": [
    "#Performing Feature Selection on Training Dataset\n",
    "print(\"Number of Features in training dataset(before feature selection by Variance Threshold):-\",X_train.shape[1])\n",
    "X_train_temp = X_train.copy(deep=True)  # Make a deep copy of the Training Data dataframe\n",
    "selector = VarianceThreshold(0.12)\n",
    "selector.fit(X_train_temp)\n",
    "X_res = X_train_temp.loc[:, selector.get_support(indices=False)]\n",
    "X_train=X_res\n",
    "print(\"Number of Features in training dataset(after feature selection by Variance Threshold):-\",X_train.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between each feature and class variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find most important features relative to target through correlation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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4UGFmpzUsf6SZHS7phD4fm+V7ulYWKtsxcn2zLVQkPRdXpKM6TzNrbIJYoj2l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\n9dgIX5DNTaBTpD3h3ro34pYuF+NTv43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l6b1mdnTtjW0UTd7UCrcn6GByXXBasmS7HsOk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\nZQb5P5/yyQidtU+rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc12c6c3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "traini = pd.concat([X_train, Y_train], axis=1, join='inner')\n",
    "\n",
    "# Find most important features relative to target i.e finding correlation of every individual feature i.e independent variable with dependent variable and then sorting them and using the features that have maximum correlation\n",
    "print(\"Find most important features relative to target through correlation\")\n",
    "corr = traini.corr()\n",
    "corr.sort_values([\"class_label\"], ascending = False, inplace = True)\n",
    "\n",
    "#Selecting top-20 features\n",
    "corr.class_label[1:20].plot(kind='bar',colors='RGYBC')\n",
    "top_features=corr.class_label[1:20].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top 20 Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['var_waistAccelerationX', 'mean_waistAccelerationY', 'var_headAccelerationY', 'var_rankleMagneticFieldX', 'mean_waistAngularVelocityZ', 'var_sternumAccelerationY', 'var_waistAccelerationY', 'var_rthighMagneticFieldX', 'var_sternumAccelerationX', 'mean_headAccelerationY', 'var_waistAngularVelocityZ', 'var_rthighAccelerationZ', 'var_sternumAngularVelocityZ', 'mean_sternumAccelerationY', 'mean_rthighAccelerationZ', 'var_rankleMagneticFieldZ', 'var_waistMagneticFieldX', 'var_lthighMagneticFieldX', 'mean_waistAngularVelocityX']\n"
     ]
    }
   ],
   "source": [
    "features=[]\n",
    "columns=top_features.index\n",
    "for col in columns:\n",
    "    features.append(col)\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select same features in Training and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n",
      "Index(['var_waistAccelerationX', 'mean_waistAccelerationY',\n",
      "       'var_headAccelerationY', 'var_rankleMagneticFieldX',\n",
      "       'mean_waistAngularVelocityZ', 'var_sternumAccelerationY',\n",
      "       'var_waistAccelerationY', 'var_rthighMagneticFieldX',\n",
      "       'var_sternumAccelerationX', 'mean_headAccelerationY',\n",
      "       'var_waistAngularVelocityZ', 'var_rthighAccelerationZ',\n",
      "       'var_sternumAngularVelocityZ', 'mean_sternumAccelerationY',\n",
      "       'mean_rthighAccelerationZ', 'var_rankleMagneticFieldZ',\n",
      "       'var_waistMagneticFieldX', 'var_lthighMagneticFieldX',\n",
      "       'mean_waistAngularVelocityX'],\n",
      "      dtype='object')\n",
      "19\n",
      "Index(['var_waistAccelerationX', 'mean_waistAccelerationY',\n",
      "       'var_headAccelerationY', 'var_rankleMagneticFieldX',\n",
      "       'mean_waistAngularVelocityZ', 'var_sternumAccelerationY',\n",
      "       'var_waistAccelerationY', 'var_rthighMagneticFieldX',\n",
      "       'var_sternumAccelerationX', 'mean_headAccelerationY',\n",
      "       'var_waistAngularVelocityZ', 'var_rthighAccelerationZ',\n",
      "       'var_sternumAngularVelocityZ', 'mean_sternumAccelerationY',\n",
      "       'mean_rthighAccelerationZ', 'var_rankleMagneticFieldZ',\n",
      "       'var_waistMagneticFieldX', 'var_lthighMagneticFieldX',\n",
      "       'mean_waistAngularVelocityX'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Selecting Features for training dataset\n",
    "X_train=X_train[features]\n",
    "print(X_train.shape[1])\n",
    "print(X_train.columns)\n",
    "\n",
    "#Selecting Same Features for testing dataset\n",
    "X_test=X_test[X_train.columns]\n",
    "print(X_test.shape[1])\n",
    "print(X_test.columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Models and Comparing them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating training and cross-validation dataset\n",
    "X_train,X_CV,Y_train,Y_CV=train_test_split(X_train,Y_train,test_size=0.30)\n",
    "\n",
    "\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    #plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "results_sensitivity={}\n",
    "results_specitivity={}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 97.2222222222 Specificity 100.0\n",
      "Error Rate:\n",
      "False Positive Rate- 0.0 True Positive Rate- 2.77777777778\n",
      "Confusion matrix, without normalization\n",
      "[[108   0]\n",
      " [  1  35]]\n",
      "Normalized confusion matrix\n",
      "[[ 1.          0.        ]\n",
      " [ 0.02777778  0.97222222]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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WrA077JADuefuu+jVawXGPPt82eXYfMz64nNOPuCHzJk9i4Y5c9hix13Y52fHc/GpR/Pi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\nZcWhZGZZcSiZWVb+F2mBddioAQ06AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc12c6c3390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vdv2B2jpLV8ClEfHHKus4dR3WcTtwYES8JmkkMCRnXtVlRbrukyIiN7yQ1LWG\n67VqePfNatM4YGdJWwFIai6pOzAF2EJSt7Tdj9fw/qdJvl68cvymFfAZSS+o0hjgqJyxqk7plyj8\nB/iBpGaSWpLsKq5NS2C2pCbA8CrzDpHUKK15S+CddN0npO2R1F3SBnmsx2rAPSWrNRExN+1x3Ctp\nvXTyryPiXUnHAo9KmgeMBfqsZhGnALdIOprkLpsnRMSLkp5PD7n/Kx1X6gW8mPbUPgeOiIiJku4H\nJgEzSHYx1+a/gZfS9m+wcvi9AzxLcv+q4yNisaQ/kYw1TUxvrjcXODC/n47ly9e+mVmmePfNzDLF\noWRmmeJQMrNMcSiZWaY4lMwsUxxKZpYpDiUzyxSHkpllyv8DPoOoMxvUB9YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc128d432b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# Applying Logistic Regression\n",
    "log_reg=LogisticRegression()\n",
    "log_reg=log_reg.fit(X_train, Y_train)\n",
    "log_pred=log_reg.predict(X_CV)\n",
    "score=accuracy_score(Y_CV,log_pred)\n",
    "#print(score)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, log_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['Logistic']=sensitivity\n",
    "results_specitivity['Logistic']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,log_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 77.7777777778 Specificity 98.1481481481\n",
      "Error Rate:\n",
      "False Positive Rate- 1.85185185185 True Positive Rate- 22.2222222222\n",
      "Confusion matrix, without normalization\n",
      "[[106   2]\n",
      " [  8  28]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.98148148  0.01851852]\n",
      " [ 0.22222222  0.77777778]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc128d46160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc128d46518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Decision Tree\n",
    "decision_reg = DecisionTreeClassifier(criterion='gini',\n",
    "                                            max_depth=10, max_leaf_nodes=23, splitter='best')\n",
    "decision_reg=decision_reg.fit(X_train, Y_train)\n",
    "decision_pred=decision_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, decision_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['DT']=sensitivity\n",
    "results_specitivity['DT']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,decision_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 97.2222222222 Specificity 96.2962962963\n",
      "Error Rate:\n",
      "False Positive Rate- 3.7037037037 True Positive Rate- 2.77777777778\n",
      "Confusion matrix, without normalization\n",
      "[[104   4]\n",
      " [  1  35]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.96296296  0.03703704]\n",
      " [ 0.02777778  0.97222222]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc128d652b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ReB+GAVvdvXe8/svMrHUS25FS0BXdkqwaZrYgfv8m8DjRA+dWu/useP7xQCdg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc128d7bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Gaussian Naive Bayes\n",
    "naivebay_reg = GaussianNB()\n",
    "naivebay_reg=naivebay_reg.fit(X_train, Y_train)\n",
    "naivebay_pred=naivebay_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, naivebay_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['NB']=sensitivity\n",
    "results_specitivity['NB']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,naivebay_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 0.0 Specificity 100.0\n",
      "Error Rate:\n",
      "False Positive Rate- 0.0 True Positive Rate- 100.0\n",
      "Confusion matrix, without normalization\n",
      "[[108   0]\n",
      " [ 36   0]]\n",
      "Normalized confusion matrix\n",
      "[[ 1.  0.]\n",
      " [ 1.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc121b371d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hwCOSFgKTgH02sohLgbslnUd2l82LIuJpSU/mh9z/no8r7Q08nffUPgTOjogX\nJI0EXgLmkO1ibs7/AZ7J209l3fB7HfgH2f2rLoyIjyX9hmys6YX85noLgFPr9tOxuvK1b2aWFO++\nmVlSHEpmlhSHkpklxaFkZklxKJlZUhxKZpYUh5KZJcWhZGZJ+f83EoQDBY9NJAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc121b25d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Support Vector Machines\n",
    "svm_reg = SVC()\n",
    "svm_reg=svm_reg.fit(X_train, Y_train)\n",
    "svm_pred=svm_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, svm_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "cm=confusion_matrix(Y_CV,svm_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaboost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 94.4444444444 Specificity 99.0740740741\n",
      "Error Rate\n",
      "False Positive Rate- 0.925925925926 True Positive Rate- 5.55555555556\n",
      "Confusion matrix, without normalization\n",
      "[[107   1]\n",
      " [  2  34]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.99074074  0.00925926]\n",
      " [ 0.05555556  0.94444444]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc121aa74a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BiIhyYEl6j1+uA9PXy+l0S5KQ2gy4LyI+S7fxQB771FvSL0gOEVsCE3OW3Z1e\nMf+WpHfSfTgQ6JMz3tQq3fbMPLZleXIoWb4+j4h+uTPS4Pk0dxbwaER8a412/YCaukpXwNiIuHGN\nbZy1Edu4FRgeEdMkjQKG5Cxbc12Rbvv0iMgNLyR1qeZ2rQo+fLOa9Bywt6SuAJI2ldQNmAFsL2nH\ntN231vPzj5N8vXjF+M3mwMckvaAKE4ETc8aqOqZfovAf4EhJzSVtRnKouCGbAQskNQFGrLHsaEmN\n0pp3AN5Mt31a2h5J3SS1yGM7Vg3uKVmNiYiFaY/jDkmbpLMvioiZkk4GJkhaBEwCeq9jFWcCN0ka\nTfKUzdMi4llJT6en3B9Ox5V6As+mPbVPgG9HxBRJdwFTgbkkh5gbcjHwfNr+FSqH35vAkyTPrzo1\nIpZJ+gPJWNOU9OF6C4Hh+f3XsXz53jczyxQfvplZpjiUzCxTHEpmlikOJTPLFIeSmWWKQ8nMMsWh\nZGaZ4lAys0z5f2SXq/dlUDvVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc121a669e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying AdaBoost Classifier\n",
    "ada_reg = AdaBoostClassifier(random_state=2, n_estimators=500)\n",
    "ada_reg=ada_reg.fit(X_train, Y_train)\n",
    "ada_pred=ada_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, ada_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['Adaboost']=sensitivity\n",
    "results_specitivity['Adaboost']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,ada_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 86.1111111111 Specificity 100.0\n",
      "Error Rate:\n",
      "False Positive Rate- 0.0 True Positive Rate- 13.8888888889\n",
      "Confusion matrix, without normalization\n",
      "[[108   0]\n",
      " [  5  31]]\n",
      "Normalized confusion matrix\n",
      "[[ 1.          0.        ]\n",
      " [ 0.13888889  0.86111111]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc121982a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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69BKwvaSeAJJaS9oQGA2sJ6lH3u7QZbz/KbKvF68Zv1kV+JKsF1RjKHBkwVhV\nl/xLFJ4Fvi+plaS2ZLuKy9MWmCSpBTB4sXkHSWqW17w+8F6+7uPz9kjaUNIqRazH6sA9Jas3ETEl\n73HcIWmlfPIZEfG+pGOAhyVNBYYBfZeyiJOA6yUdRXaXzeMj4kVJz+eH3B/Nx5U2Al7Me2pfAT+M\niFGS7gJeA8aR7WIuz/8AL+ft32TR8HsPeIbs/lXHRcQ3kv5GNtY0Kr+53hRgv+J+OlYsX/tmZknx\n7puZJcWhZGZJcSiZWVIcSmaWFIeSmSXFoWRmSXEomVlSHEpmlpT/D8sbmvLiJbUnAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc121985080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Gradient Boosting Classifier\n",
    "params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2, 'learning_rate': 0.01}\n",
    "gb_reg = ensemble.GradientBoostingClassifier(**params)\n",
    "gb_reg=gb_reg.fit(X_train, Y_train)\n",
    "gb_pred=gb_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, gb_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['GBT']=sensitivity\n",
    "results_specitivity['GBT']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,gb_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 83.3333333333 Specificity 99.0740740741\n",
      "Error Rate:\n",
      "False Positive Rate- 0.925925925926 True Positive Rate- 16.6666666667\n",
      "Confusion matrix, without normalization\n",
      "[[107   1]\n",
      " [  6  30]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.99074074  0.00925926]\n",
      " [ 0.16666667  0.83333333]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1480b0c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZnhxKlq/PImJA7oQ0eD7NnQQ8GhHfrdRuAFBTZ+kKGBMRN1Rax+kbsI5bgZER\n8ZKk0cDQnHmVlxXpuk+JiNzwQlKXaq7XquDdN6tJk4BdJHUDkNRMUg/gdWBrSV3Tdt9dx/sfJ/l5\n8Yrxm42BT0h6QRUmAMfmjFV1TH9E4V/AtyQ1ldSSZFdxfVoC8yU1Ao6qNO8wSQ3SmrcB3kjXfVLa\nHkk9JDXPYz1WDe4pWY2JiAVpj+MOSRulky+IiBmSTgAekrQQmAj0XcsiTgNulHQcyV02T4qIZyU9\nkx5yH5+OK/UGnk17akuB70XEVEl3AdOA2SS7mOvzc+C5tP3LrBl+bwBPkdy/6sSI+FzSn0jGmqam\nN9dbAIzM72/H8uVr38wsU7z7ZmaZ4lAys0xxKJlZpjiUzCxTHEpmlikOJTPLFIeSmWWKQ8nMMuX/\nAcjGm9B+mitpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1219ed2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Random Forest Classifier\n",
    "rf_reg = RandomForestClassifier(random_state=1)\n",
    "rf_reg=rf_reg.fit(X_train, Y_train)\n",
    "rf_pred=rf_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, rf_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['RForest']=sensitivity\n",
    "results_specitivity['RForest']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,rf_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Majority Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 91.6666666667 Specificity 99.0740740741\n",
      "Error Rate:\n",
      "False Positive Rate- 0.925925925926 True Positive Rate- 8.33333333333\n",
      "Confusion matrix, without normalization\n",
      "[[107   1]\n",
      " [  3  33]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.99074074  0.00925926]\n",
      " [ 0.08333333  0.91666667]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1218be7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc12187bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Applying Majority Voting Concept\n",
    "majority_class = VotingClassifier(estimators=[('lr', ada_reg),\n",
    "                                    ('gnb', gb_reg),('naive_bayes',naivebay_reg)],\n",
    "                                    voting='hard')\n",
    "majority_class = majority_class.fit(X_train, Y_train)\n",
    "majority_pred=majority_class.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, majority_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['MVoting']=sensitivity\n",
    "results_specitivity['MVoting']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,majority_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f7ac3AtcMLLuGRw8iNswsH5VVSX4SOBO4pK/lORnYk0xyZ5J3J/lKnzvP69unkvxV3/6n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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc120ae68d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(results_sensitivity)), list(results_sensitivity.values()),color='RGBYC',width=0.3)\n",
    "plt.xticks(range(len(results_sensitivity)), list(results_sensitivity.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mlo/KqiQ/BZwFXNzX8pwM7EkmuSPJu5N8pc+d5/XtU0n+vG//oyTfmGMANbuqOiL+AfeP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\ngwx3SWqQ4S5JDfr/PDsc3Xgn2UUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc120ae65f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(results_specitivity)), list(results_specitivity.values()),color='RGBYC',width=0.3)\n",
    "plt.xticks(range(len(results_specitivity)), list(results_specitivity.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
